Triple
T4651122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transformer |
E102296
|
entity |
| Predicate | foundationFor |
P1450
|
FINISHED |
| Object | T5 |
E435867
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: T5 | Statement: [Transformer, foundationFor, T5]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: T5 Context triple: [Transformer, foundationFor, T5]
-
A.
T5
T5 is a major passenger terminal at London Heathrow Airport, primarily serving British Airways and Iberia flights.
-
B.
T5
T5 is a former passenger terminal of Berlin Brandenburg Airport that handled commercial air traffic before being closed to operations.
-
C.
T5
T5 is a tram line of the Trambesòs light rail network serving the Barcelona metropolitan area.
-
D.
T5
T5 is one of the lines of the Athens tram system, providing light-rail transit service along part of the city’s coastal and urban corridor.
-
E.
T5
chosen
T5 is a Transformer-based text-to-text language model developed by Google that treats every NLP task as converting input text to output text.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd43d71a308190afea7280841b0de8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd630343f88190954d19fcd18a5864 |
completed | March 20, 2026, 3:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfae7636881908244b86cba1c66b7 |
completed | March 21, 2026, 1:56 a.m. |
Created at: March 20, 2026, 1:14 p.m.